Identification of tissue-specific, abiotic stress

23 Pages·2011·2.46 MB·English

Tillettetal.BMCPlantBiology2011,11:86
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RESEARCH ARTICLE Open Access
Identification of tissue-specific, abiotic stress-
responsive gene expression patterns in wine
grape (Vitis vinifera L.) based on curation and
mining of large-scale EST data sets
Richard L Tillett1, Ali Ergül2, Rebecca L Albion1, Karen A Schlauch1, Grant R Cramer1 and John C Cushman1*
Abstract
Background: Abioticstresses,suchaswaterdeficitandsoilsalinity,resultinchangesinphysiology,nutrientuse,and
vegetativegrowthinvines,andultimately,yieldandflavorinberriesofwinegrape,VitisviniferaL.Large-scaleexpressed
sequencetags(ESTs)weregenerated,curated,andanalyzedtoidentifymajorgeneticdeterminantsresponsibleforstress-
adaptiveresponses.Althoughrootsserveasthefirstsiteofperceptionand/orinjuryformanytypesofabioticstress,EST
sequencinginroottissuesofwinegrapeexposedtoabioticstresseshasbeenextremelylimitedtodate.Toovercomethis
limitation,large-scaleESTsequencingwasconductedfromroottissuesexposedtomultipleabioticstresses.
Results: A total of 62,236 expressed sequence tags (ESTs) were generated from leaf, berry, and root tissues from
vines subjected to abiotic stresses and compared with 32,286 ESTs sequenced from 20 public cDNA libraries.
Curation to correct annotation errors, clustering and assembly of the berry and leaf ESTs with currently available V.
vinifera full-length transcripts and ESTs yielded a total of 13,278 unique sequences, with 2302 singletons and 10,976
mapped to V. vinifera gene models. Of these, 739 transcripts were found to have significant differential expression
in stressed leaves and berries including 250 genes not described previously as being abiotic stress responsive. In a
second analysis of 16,452 ESTs from a normalized root cDNA library derived from roots exposed to multiple, short-
term, abiotic stresses, 135 genes with root-enriched expression patterns were identified on the basis of their
relative EST abundance in roots relative to other tissues.
Conclusions: The large-scale analysis of relative EST frequency counts among a diverse collection of 23 different
cDNA libraries from leaf, berry, and root tissues of wine grape exposed to a variety of abiotic stress conditions
revealed distinct, tissue-specific expression patterns, previously unrecognized stress-induced genes, and many novel
genes with root-enriched mRNA expression for improving our understanding of root biology and manipulation of
rootstock traits in wine grape. mRNA abundance estimates based on EST library-enriched expression patterns
showed only modest correlations between microarray and quantitative, real-time reverse transcription-polymerase
chain reaction (qRT-PCR) methods highlighting the need for deep-sequencing expression profiling methods.
Background function, multiple transcriptomic approaches have been
The study of gene function in the wine grape (Vitis vini- developed [4,5], including expressed sequence tags
fera L.) has been fundamentally advanced by the avail- (ESTs) [6], massively parallel signature sequencing
ability of whole genome sequences of two Pinot Noir (MPSS) [7], small RNA deep sequencing [8], Illumina
cultivars (clones 115 and PN40024) [1,2] as well as sequencing [9], and multiple oligonucleotide microarray
BAC-based physical maps [3]. To study wine grape gene platforms [10-13].
Most V. vinifera varieties are ranked as moderately
*Correspondence:[email protected] sensitive to sensitive to salinity stress [14-17] with Cl-
1DepartmentofBiochemistryandMolecularBiology,MS330,Universityof anion toxicity having the greatest impact on growth and
Nevada,Reno,NV89557-0330,USA vine health [18]. In contrast, V. vinifera is relatively
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water-deficit stress tolerant. Regulated-deficit irrigation leaf and berry tissues from vines subjected to abiotic
can be used advantageously to inhibit vine growth with- stresses (e.g., salinity, cold, heat, water deficit, and
out significant effects on fruit yield and has been anoxia). These were compared with 32,286 ESTs within
reported to improve grape quality through the elevation 20 libraries derived from leaf and berry tissues deposited
of a variety of metabolites including anthocyanins and in the public databases. Clustering and assembly of leaf
proanthocyanins [19-22]. mRNA and enzyme expression and berry ESTs with all available V. vinifera full-length
profiles during development and in response to abiotic transcripts and ESTs returned a total of 13,278 unique
stress effects have been studied intensively in wine grape sequences, with 2302 singletons and 10,976 clusters
berries [11,12,23-30]. Additional studies have examined mapping to known gene models. Of these 10,976 unique
mRNA expression patterns in response to abiotic stres- clusters, 739 transcripts were found to have significant
ses in leaves and shoot tissues [10,31], plant-pathogen differential expression among the libraries examined.
interactions [13,32,33], and the events associated with Comparison of in silico digital expression analysis with
Vitis bud endodormancy [34-36]. transcript abundance estimates obtained by Affymetrix
The roots of terrestrial plants are vital organs for the Vitis GeneChip® genome microarrays and quantitative
acquisition of water and essential minerals. As such, real-time reverse transcription-polymerase chain reac-
roots serve as the first site of perception and/or injury tion (qRT-PCR) revealed that EST frequency counts
for many types of abiotic stress, including water defi- were in moderate agreement with microarray or qRT-
ciency, salinity, nutrient deficiency, and heavy metals PCR analysis. Given the relative lack of ESTs available
[37-39]. Vitis roots also accumulate a number of unique for grape root tissues, 16,452 ESTs were sequenced
stilbene and oligostilbene defense compounds, chemical from roots of young vines (10 cm in length), grown
species not found in seed or other phytoalexin-rich tis- under unstressed conditions as well as under cold, sali-
sues [40,41]. Despite the importance of roots, the study nity, and water deficit stress. The major categories of
of V. vinifera root tissues has been rather limited in genes expressed in root tissues were defined and 135
contrast to the study of berry tissues. In a comparative genes with root-specific or highly enriched root expres-
EST study, Moser and colleagues generated 1555 ESTs sion patterns were identified.
from V. vinifera cv. Pinot Noir root tissue and found
them enriched for genes with functions in primary Results
metabolism and energy [42]. Using a 12 K CombiMatrix EST library analysis from abiotically stressed tissues of
custom array, Mica and colleagues profiled the expres- Vitis vinifera
sion of microRNAs (miRNAs), small (19-24 nt) non- cDNA libraries derived from abiotically stressed leaves
coding RNAs that negatively regulate gene expression (Library ID 10208) and berries (Library ID 12435) of V.
post-transcriptionally in multiple organs. This study vinifera cv. Chardonnay, were sequenced to generate
showed that roots had nine and four miRNAs with 24,400 and 21,384 ESTs, respectively (Table 1). In addi-
either significantly increased or decreased relative abun- tion, a total of 16,452 ESTs were sequenced from a nor-
dance, respectively, relative to leaves and early inflores- malized cDNA library synthesized from Magenta box
cences [8]. A framework physical or genetic map has grown root tissues from cv. Cabernet Sauvignon
also been developed for wine grape, using resistant and exposed to control, water deficit, cold, and salinity stress
susceptible crosses, to locate genetic determinants asso- conditions (see Methods section for details) (Library ID
ciated with resistance to the root pathogen phylloxera 22274). In total, 66,236 expressed sequence tags (ESTs)
[43]. EST transcriptional profiling has recently been were generated (Table 1). The leaf and berry libraries
used to identify genes that might be involved in resis- were described previously in the context of flower and
tance to Rhizobium vitis in the semi-resistant Vitis berry development [6]. In addition, five unstressed leaf
hybrid ‘Tamnara’ [44]. libraries, representing a total of 8642 ESTs, 13 whole
In grapevine, more than 350,000 EST sequences have berry with seeds libraries derived from unstressed source
been generated and analyzed to identify gene expression tissues at various stages of berry development, repre-
related to a wide range of processes including berry senting a total of 31,840 ESTs, and two root libraries,
development in wine grape [30,45] and in table grape representing a total of 1657 ESTs, present within the
[46], tissue-specific gene expression [6,42], the fulfill- UniGene database [48] were compiled (Table 1). These
ment of chilling requirements in dormant grape buds EST collections were used as tools to identify transcripts
[34], and the characterization of resistance to pathogens encoding abiotic stress responsive transcripts in leaves
such as Xylella fastidiosa [47] and Rhizobium vitis [44]. and berries and root-specific or root-enriched
To discern how steady-state transcript accumulation transcripts.
changes in response to multiple environmental stress To create up-to-date annotations, each EST was
treatments, we generated a total of 45,784 ESTs from matched with the corresponding “tentative consensus”
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Table 1cDNA LibraryAttributes
Tissue dbEST cDNA SubmittedDescription Developmental ESTsas Unique
LibraryID Orientation Stage perdbEST clones
(5’/3’)
Stressed 10208 both Anexpressedsequencetagdatabaseforabioticstressed Juvenile&adult 24,400 21,499
Leaf leavesofVitisviniferacv.Chardonnay
Stressed 12435 both Anexpressedsequencetagdatabaseforabioticstressed Mixed:8,9,11,13,15, 21,384 18,963
Berry berriesofVitisviniferacv.Chardonnay 16weeksDAF
Leaf 12752 both CabernetSauvignonLeaf-CA32EN Mid-season 2,669 1,465
Leaf 12753 both CabernetSauvignonLeaf-CA48EN Mid-season 2,051 1,104
Leaf 12948 both CabernetSauvignonLeaf-CA48LN LateSeason 2,248 1,441
Leaf 12949 both CabernetSauvignonLeaf-CA41LN LateSeason 1,146 739
Leaf 14446 5’ GrapeLeafpBluescriptLibrary Juvenile 528 528
Leafsubtotal 8642 5277
Berry 4059 both GrapeberriesLambdaZapIILibrary Véraison 105 96
Berry 8669 3’ GreenGrapeberriesLambdaZapIILibrary Pre-véraison 1,989 1,989
Berry 8670 3’ RipeningGrapeberriesLambdaZapIILibrary Post-véraison 3,268 3,267
Berry 8671 both VéraisonGrapeberriesLambdaZapIILibrary Véraison 96 96
Berry 11063 3’ VéraisonGrapeberriesSuperScriptTMPlasmidLibrary Véraison 623 623
Berry 11064 3’ VéraisonGrapeberriesLambdaZapIILibrary Véraison 1,691 1,691
Berry 12754 both CabernetSauvignonBerry-CAB2SG Pre-véraison 4,429 2,339
Berry 13015 both CabernetSauvignonBerryStageI-CAB3 Pre-véraison 3,414 1,955
Berry 13016 both CabernetSauvignonBerry-CAB4 Pre-véraison 3,836 2,155
Berry 13017 both CabernetSauvignonBerryPost-Véraison-CAB7 Post-véraison 3,558 1,911
Berry 14444 5’ GrapeBerrypSPORT1Library Véraison 1,743 1,743
Berry 20043 n.d. Clusters4cm(VvC3) Pre-véraison 4,053 4,053
Berry 20044 n.d. BerriesVéraisonstage(VvC4) Véraison 3,035 3,035
Berrysubtotal 31840 24953
Root 14445 5’ GrapeRootpSPORT1Library Oneyear-oldroot 1,555 1,555
Root 16696 n.d. VitisviniferaCabernetSauvignonroot n.d. 102 102
Stressed 22274 5’ VVM-NormalizedCabernetSauvignonroot Youngvines 16,452 16,452
Root
Total 104,402 88,828
Librariesgeneratedorusedinthepresentstudy.TheStressedLeaf(SL)andStressedBerry(SB)librariesweregeneratedpreviously[6],theStressedRoot“VVM”
librarywasgeneratedspecificallyforthisstudy,andallotherlibrarieswereobtainedfromthedbESTdatabasemaintainedbytheNCBI.Tissues,dbESTlibrary
identifier,sequencingdirection,andlibrarydescriptionsareprovided.UniquecloneswereidentifiedfromtheESTsofbidirectionallysequencedlibrariesas
describedinthe“Methods”and“Results”sections.
n.d.notdetermined
(TC) contig sequence from the Vitis vinifera Gene Index Because the mixed stress normalized root library was
(VvGI, version 6, July 30, 2008, Dana Farber Cancer generated using a normalization technique that would,
Institute) [49] and predicted peptide sequences from the in effect, reduce the apparent expression of the most
Genoscope 8.4X Vitis vinifera cv. Pinot Noir (GSVIV) abundant transcripts, and because few other unstressed
genome assembly, August 8, 2007 [1]. A newer version root ESTs were available for comparison, characteriza-
of VVGI (7.0, 4/17/2010) was released since this analysis tion of the genes in the root EST library was performed
was undertaken. However, this release is substantially in a separate analysis.
similar to 6.0, containing the same 25,497 gene models Identifying EST redundancy
derived from the NCBI RefSeq source and only 4851 In estimation of gene expression patterns inferred from
additional ESTs and was not expected to substantially EST frequencies, which are the number of times the
alter the findings presented. A newer 12X coverage draft transcript of gene x is observed in relation to the total
i
of the Vitis vinifera genome has also become available. number of random observations of all genes, (x / ∑x),
i
However, some gene models annotated in this 12X draft any ESTs from a single clone sequenced from both the
were found to contain greater frequencies of intron- 5’ and 3’ directions must be counted exactly once to
exon splices not supported by EST evidence (data not avoid overestimation of the frequency of genes. cDNA
shown) and, therefore, the 12X draft was not used. library sequencing strategies varied among sources, with
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some ESTs being generated from only single-pass 5’ or generally contained four plates (I-IV), and 80% of plates
3’ reads, whereas other libraries were subjected to bi- were sequenced from both 5’ and 3’ directions. When
directional and/or same-direction re-sequencing of the forward and reverse pairs of ESTs in Library ID
picked clones. In the abiotically stressed leaf library 12948 were organized by their 96-well plate well order
(Library ID 10208), 2802 ESTs had been sequenced (A1,A2,...,A12,...,H1,...,H12), various patterns of “well
twice (representing 1401 paired reads). Another 2250 slip” were identified, wherein the gene ID for well A9
ESTs had been sequenced three times (750 triplets). (5’) matched the gene ID of well A10 (3’), A10 (5’)
Eliminating this redundancy reduced the EST total from matched A11 (3’), and so forth. The distance of these
24,400 ESTs to 21,499 unique clones (Table 1). The “well slips” was neither uniform nor consistent.
GSVIV gene identifiers of paired clone ends were com- To determine all pairs of ESTswith incorrectly paired
pared with the expectation that gene IDs would agree wells,amethodwasdevisedthat wouldidentifyrobustly
between multiple ESTs from the same transcript. Of the “well slips” of non-uniform distances, analogous to the
1401 pairs of clones from this abiotically stressed leaf dot-plot method of localnucleotide sequence alignment
library, 114 (7%) pairs matched the annotation of differ- [51].Inthismethod,thegeneIDsofESTswerearranged
ent genes and the ID with greatest confidence score was from A1-H12 for each 5’ and 3’ plate and plotted along
retained. Similarly, only 60 of the 750 triplicated clones two axes with a dot designating wherever the gene IDs
(8%) within this library were in disagreement as to gene wereidentical(Figure1).Thedotplotprovedeffectiveat
identity. The total clone redundancy and gene assign- identifying forward-reverse pairing in plates with “well
ment error rates were similar for ESTs from the stressed slips,” such as in Figure 1A, wherein the four forward
berry library (Library ID 12435). In this EST collection, andreverse platesof “batch09”inleafLibrary ID 12948
2402 ESTs had been sequenced twice (representing were plotted in the order 1f, 1r, 2f, 2r, 3f, 3r, 4r along
1201 pairs), 1821 ESTs had been sequenced three times both the × and y axes. The main diagonal bisecting the
(607 triplets), and two clones had been sequenced four plot,wheretheorderedlistisidenticaltoitself,isflanked
times each. Eliminating this redundancy reduced the byfouroffset diagonalsthatillustratewheretheforward
EST total from 21,384 ESTs to 18,963 unique clones and reverse plate pairs match (1f≈1r, 2f≈2r, etc.). The
(Table 1). Of the 1201 pairs and 607 triplicates, 107 matchingclearlydistinguishedpairsofplatesthroughthe
(9%) and 68 (11%) were in disagreement, respectively. variable“wellslips”inLibraryID12948.
This method of redundancy elimination was extended This matching process was repeated for all plate
next to those bi-directionally sequenced clones from the batches (generally four forward and four reverse plates
non-stressed leaf and berry libraries obtained from the per batch) of the libraries listed in Table 2 and other
UniGene database (Table 1). Many errors were found in error types besides the “well slips” seen in Library ID
the annotated compositions of leaf (Library IDs: 12752, 12948 were uncovered. Some plates were duplicated, as
12753, 12948, and 12949) and berry libraries (Library seen in Figure 1B, wherein all combinations of the for-
IDs: 12754, 13015, 13016 and 13017). The errors and ward and reverse of four individual plates matched in
the corrections made are explained below as presented berry Library ID 12753 (1f≈1r≈2f≈2r). Were these errors
in Figure 1 and summarized in Table 2. For the Caber- not identified, the ESTs of plate 1 and 2 would have
net Sauvignon leaf library CA48LN (Library ID 12948), been added both to the frequency totals of the genes
we were able to organize 1486 ESTs into 743-paired therein (i.e., counting twice what should only be
reads. Within these pairs, > 68% (509) could not be counted once), resulting in an overestimation of the fre-
assigned to the same gene. Similarly, high rates of dis- quency of those transcripts in the library. Other pairs of
agreement were found within other libraries listed in plates showed a less complete duplication pattern as
Table 2. As these rates were higher than those observed seen as the inchoate diagonals between plates 1 and 2
in paired reads from abiotically stressed leaf or berry (pink) and between 2 and 3 (purple) in Figure 1E, and
libraries, the cause or causes of these higher error rates all four plates (purple) in Figure 1F. In other cases, a
were investigated further. plate did not match the annotated reverse, but a differ-
The cDNA libraries presented in Table 2 were bidirec- ent plate instead, such as the pair-swapping of Library
tionally sequenced and had annotation that allowed for ID 12948 (3f≈4r and 4f≈3r) in Figure 1C and triplication
the partial reconstruction of the workflow by which they (2f≈2r≈3r) / mis-pairing (3f≈1r) in berry Library ID
were prepared and sequenced originally [50] with clone 13016 (Figure 1D). Where identified, these partial dupli-
names deposited to NCBI such as “CA48LN09IF-A9, cations and mismatched plates were handled just as the
5’end.” This annotation identifies the library “CA48LN,” full duplications were, with the EST counts reduced to
a batch number ("09,” the plate within that batch (I), reflect the true number of independent clones involved.
location on a 96-well plate (A9) and direction (5’). All The same analytical method was then extended to
ESTs in a given library shared the library stem, batches compare every plate in a library to all other plates in
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A B C
D E F
G H
Figure 1 Correcting erroneous EST identities in bi-directionally sequenced leaf and berry libraries with dot plots. Contig names
assignedtoESTsfrombi-directionallysequencedlibrarieswereplottedintwodimensionstoidentify“motifsofself-similarity”analogoustodot-
plotsequencealignments.Thesequencingbatch,plateorder,andwellpositionwererecapitulatedfromdbESTsubmissionfilesasasequential
listarrangedas1f,1r,2f,2r,3f,3r,4f,4r,andplottedagainstitselfinthexandyaxes.A)DiagonalsindicatefoursetsofplatesfromLibraryID
12948,batch8arenamedandpairedcorrectly(blue);B)LibraryID12753,batch1,allcombinationsofplates1f,1r,2fand2rareduplicates
(salmon),plates3and4arecorrectlypaired(blue);C)LibraryID12948,batch10plate1fmatches1r(blue),plate2fand2rdidnotmatch,plate
3fmatches4r(salmon),4fmatches3r(magenta);D)BerryLibraryID13016,batch1,plate3rmatcheswith2fand2r(salmon),1rmatcheswith
3f(magenta),1fhasnomatch,plate4ispairedcorrectly(blue);E)LibraryID13017,batch2,Plates1and2displaypartialmatching(pink),plates
2and3alsopartiallymatch(purple);F)BerryLibraryID13017batch3,partialmatchingbetweenallfourplates(purple);G)BerryLibraryID
13015,batch2,plate1matchesbatch5plate1r(salmon);otherplatematcherrorsarealsoapparentinlowerrighthandquandrant(magenta);
H),LeafLibraryID12752,batch5,plate4rmatchesBerryLibraryID12754,batch5,plates4fr(salmon).
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Table 2Correction oferrorsin the identifications ofESTs in asetoflibraries
Errorcategory Specificerrortype #ofErrors Seealso
Wellpairing“slips”
LeafLibraryID12948 10 Figure1A
LeafLibraryID12949 1
Incorrectplatepairings
LeafLibraryID12753 Platequadruplicated 1 Figure1B
BerryLibraryID12754 Platequadruplicated 1
LeafLibraryID12948 Platepairswap 2 Figure1C
BerryLibraryID13015 Platepairswap 2
Platetriplicated 3 Figure1G
BerryLibraryID13016 Platetriplicated 1 Figure1D
Platepairswap 1
Partialplateduplications
BerryLibraryID13016 6
BerryLibraryID13017 28 Figure1E,1F
Sequencesoriginatedfromadifferentlibrary
LeafLibraryID12752 Plateof“leaf”ESTsactuallyatriplicateofberryLib.12754ESTs 1 Figure1H
and
BerryLibraryID12754
ErrorsinthesuppliedannotationofasetofcDNAclonesthatweresequencedbidirectionallywereidentifiedandcorrectedtogenerateaccuratecountsofEST
frequency.Errorsarecategorizedbythescopeoftheerror,from“wellslips”betweensinglepairsof5’and3’96-wellplatesofESTs,throughincorrectly
identifiedpairsofplatesofincreasingscope.Thenumberoftimeseacherroroccurred(pairsorlargergroupsof96-wellplatesaffected)andwascorrectedis
shown.Errorsthatarevisualizedbydot-plotinFigure1arecross-referenced.
that library. One additional case of unexpected matching the EST frequency within each GSVIV gene ID (or Uni-
was found, where plates from one batch match the Gene ID, in cases where no GSVIV gene model could
plates of a different batch in the same library (Figure be assigned) was calculated for each leaf, berry, stressed
1G). Lastly, we extended the method to compare every leaf, and stressed berry library. The EST frequencies of
plate in every library against all other plates in all other the five leaf libraries were combined by weighted mean,
libraries, even those annotated as arising from different as were the 13 berry frequencies [52]. Differential gene
tissues. From this, a single instance was found where a expression was then calculated using the combined EST
plate in the leaf Library ID 12752 (plate 4r) was identi- frequency counts for genes using the IDEG6 web tool
cal to a pair of plates from berry Library ID 12754 (Fig- [53]. The chi-squared test (c2) was used as the test sta-
ure 1H). The genes encoded on these plates were tistic, as recommended when conducting statistical com-
consistent with those found in mature berry library (e.g., parisons of more than two groups [54]. At a p-value
cell wall proteins, ripening-related proteins, and no cutoff of < 0.001, 739 genes were estimated to have dif-
photosynthesis genes), but not a leaf library, leading to ferential expression among the libraries compared. The
the conclusion that a cDNA library misassignment error 739 genes were then organized by hierarchical cluster-
had occurred, and leading to the exclusion of these data ing, using a function of the Pearson correlation coeffi-
from our analyses. To uncover other possible library cient as the distance metric and the average
assignment errors, every plate from all libraries in the agglomeration method (Figure 2). The sets of genes
present study were compared against all other libraries clustered first between tissue type, as seen by the first
(e.g., bud tissues, petioles, flowers, and pathogen branching in the dendrogram, and then by control or
infected leaves) that were not considered for our abiotic abiotic stress condition, as seen in the next two
stress analysis, but no further spurious pairings were branches. At this distance the four clusters generally
detected (data not shown). Upon exhaustively identifying correlated to transcript abundance profiles within a sin-
all observable patterns of errors, 5’ ESTs were paired gle library type with the largest cluster of 355 transcripts
with their 3’ partners and the unique clones within each corresponding to tissues of stressed leaves (SL). The leaf
library were counted (Table 1). In total, errors in the cluster (L) contained 127 genes, whereas stressed berry
identification/annotation of 5558 of 23,351 ESTs (24%) (SB) and unstressed berry clusters (B) contained 127
were discovered from the libraries listed in Table 2. and 130 genes, respectively. The annotation, gene mod-
Estimating gene expression by EST frequency els, and relative frequencies of all 739 genes are listed
In order to measure differences in gene expression pat- by cluster in Additional Files 1, 2, 3 and 4. The high
terns among stressed and unstressed leaves and berries, number of transcripts present within the stressed leaf
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Without over-interpretation, some key differences
among the functional categories of genes prominent
within each organ/condition are clearly apparent. For
example, unstressed leaves (Figure 3A) were distin-
guished by a large proportion (28%) of primary meta-
bolic genes with some photosynthetic genes, such as
RUBISCO small subunit and plastidic photosynthetic
SL (n=355)
electron transport components being extremely over
represented. Transcripts for non-specific lipid-transfer
protein, metallothionein, early light-induced protein
(ELIP1), and several unknown genes were also highly
represented within this cluster along with 23S rRNA
L (n=127) (Additional File 2). In stressed leaf, 11% of transcripts
encoded photosynthesis-related functions, including
plastidic ATP synthase and electron transport chain
subunits, suggesting that higher demands and/or
damage might occur under stress that must be repaired
(Figure 3B). Consistent with this suggestion is the over
SB (n=127) representation of several families of low molecular heat
shock proteins. Leaves under abiotic stress expressed a
greater proportion of specific transport genes (21%)
(Additional File 1). Interestingly, the activity of transpo-
B (n=130) sons is apparently de-repressed in stressed leaves as
judged by the preponderance (7%) of a centromere-spe-
cific class of retrotransposons. Similar abiotic induction
Figure2Heat-mapandtwo-dimensionalhierarchicalclustering
of retroelements in non-germline tissue has been
ofESTfrequenciesin739differentiallyexpressedgenes
described in Solanaceous species and the ABA-induction
amongcDNAlibrariesfromstressedandunstressedleavesand
berries.ColorshownisgiveninnormalizedESTfrequencyper of the Tnt1A promoter in Arabidopsis thaliana [56].
10,000ESTs,scalefromblueatf=0towhitetoredatf>53.6 The unstressed berry cluster possessed overrepresented
(inset).Fourmajorclustersthatcorrespondtosingle-type transcripts encoding genes with functions involved in
predominancearelabeled(withthenumberofgeneswithinthe
primary metabolism, translation, cell wall-related pro-
cluster)stressedleaf(SL),leaf(L),StressedBerry(SB),Berry(B).
teins (9%), and transport (12%) (Figure 3C, Additional
File 4). In contrast, the stressed berry cluster (Figure
3D) had the highest proportion of genes annotated as
cluster might reflect the depth to which this library was “stress-responsive” (17%) including overrepresented
sequenced, the variety of abiotic stresses to which these transcripts encoding xyloglucan endotransglucosylase/
source tissues were subjected, and the diversity of tran- hydrolases, a DEAD box RNA helicase, and seed storage
scripts expressed within the grape leaf transcriptome proteins including albumins and globulins and several
under abiotic stresses [10]. highly abundant unknown proteins (Additional File 3).
Of these 739 genes with differential expression among Correlation with microarray data
the cDNA library clusters, 637 were matched success- Next, differences in transcript expression patterns esti-
fully to GSVIV gene/protein identifiers, which were then mated by EST frequency were compared with a second
matched with the annotation files associated with Vitis- platform, the Affymetrix® Vitis GeneChip® microarray.
Net [55]. VitisNet networks were combined into cate- Of the 739 transcripts described above, microarray pro-
gories of their major networks, with metabolic networks beset identifiers could be assigned for 489 of them. All
divided into primary metabolism, photosynthesis, sec- differentially expressed genes available from microarray
ondary metabolism, and hormone biosynthesis, the latter experiments in which similar stresses were imposed
category being grouped with the hormone signaling were collected. For leaf tissue, within which our stressed
category. Gene IDs that were “out-of-network”, but that leaf library included a mixture of drought, NaCl, heat,
had functional annotations associated with them in the and light stressed tissue, two experiments were used as
VitisNet master list were also incorporated into the a source for microarray data: an experiment in which
functional category designations. In Figure 3, the func- drought and salt stress were applied over a 16 d period
tional categories of genes identified within the four [10] and an experiment that analyzed rapid changes (≤
major clusters are shown. 24 h) in gene expression under osmotic stress
Tillettetal.BMCPlantBiology2011,11:86 Page8of23
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A 2% 1% C
5% 1% 10% 3% 14% 2% 2%
13% 28%
2% 12%
1%
2%
22%
2% 9%
3%
13% 3%
1%% 5%
6% 5% 3% 5% 6%
8% 9%
2%
Leaf Berry
3%
1%
D
B 2% 7% 17% 8% 16%
2%
17% 2%
21% 9% 4%
2%
2%
11% 10%
11%
5%
3% 13%
5% 6% 5% 4%
1%
2% 2%
3% 3%
2% 1% 2% 1%
Stressed Leaf Stressed Berry

Figure3FunctionalcategoriesofdifferentiallyexpressedtranscriptsidentifiedbyESTfrequencyanalysis.Functionalassignmentsof
genesfoundinthefourmajorclustersofdifferentiallyexpressedgenes.Atthechosenhierarchydepth/distance,thefourclusterscorrespond,
inlargepart,tomaximalfrequencieswithinA)Leaf,B)StressedLeaf,C)Berry,andD)StressedBerrycDNAlibraries.Assignmentsarebasedupon
thedataavailableatVitisNethttp://www.sdstate.edu/aes/vitis/pathways.cfm[93].Chartcolorsprogressclockwisefromthetop.
(mannitol), NaCl, and chilling exposure [31]. For the unlikely to be due to chance alone. Pearson correlation
berry libraries, microarray data from a drought stressed was similar (r = 0.21). In other studies comparing
berry time course experiment of Chardonnay and microarray to EST or similar tag-based technologies,
Cabernet Sauvignon [27] were compared with EST fre- modest Spearman and Pearson correlations have been
quency data. Following the example of van Ruissen and observed [58]. Following the example of Li and collea-
colleagues [57], probeset expression values were then gues, the directional concordance, which is the direc-
compared with EST frequencies using only those probe- tional agreement in either increased or decreased
sets for which significant differences were observed relative transcript abundance in response to stress treat-
between stressed and unstressed tissues in the original ment, among the 184 significant genes common to both
microarray experiments. Using this method, 184 com- microarray or EST sampling detection methods was
parisons of significantly different changes were plotted determined. In their comparison of SAGE tags with
(Figure 4). Overall correlation between the microarray microarrays in multiple human tissues, these authors
and frequency-based expression measures was modest. found 75% directional concordance among significant
The non-parametric Spearman rank correlation was genes [58]. Similarly, for our 184 shared genes, the
modestly positive, at (rs = 0.2), but with a P < 0.005, directional concordance was 69% or more than two
indicating that this similarity, while modest, is extremely agreements per disagreement.
Tillettetal.BMCPlantBiology2011,11:86 Page9of23
http://www.biomedcentral.com/1471-2229/11/86
ntrol)] 3 7 19
ess/co 29 6 81 PCR 56
str 4 RT- 4
og(2 by q 3 leaf
n [l 2 w) 2 berry
o w 1
si d/
s w
expre -10 -5 5 10 log(2 -3 -2 -1 -1 1 2 3 4 5 6 7
e r = 0.2047 -2
n -2 s
ge P = 0.005 13 -3 2
y
oarra 46 -4 28 Figure5Expressiolongo2(fwsdtr/wesws-)r beyla mteidcrogaernraeysinV.viniferaleaves
cr andberriesasdetectedbymicroarrayandqRT-PCR.Log-
Mi log [EST frequency difference (stressed/not stressed)] transformedvaluesofAffymetrix®VitisGeneChip®signalinte2nsities
2
(x-axis)andrealtime-RT-PCRexpressionlog-ratios(y-axis)of22
Figure 4 Scatterplot of EST frequencies compared with 2
genesinleaftissue(filledcircles),aswellas17genesinberrytissue
microarrayexpressionlevels.Log-transformedfrequency
2 (opencircles)ofwaterdeficit(wd)andwellwatered(ww)vines.A
distributionsofESTsfrommixedstressedleaf(e.g.,waterdeficit,
linearregressionhasslopem=0.92andPearsoncorrelationr=
NaCl,heat,highlight)andberry(waterdeficitstress)andunstressed
leafandberrytissuewerecomparedto184Affymetrix®Vitis 0.85forthetotaldatasetof39pairsoflog2-ratios[10,24].Thetotals
GeneChip®log-abundanceratiosofchilling,osmotic(mannitol), ofgenespresentineachCartesianquadrantareshowningray-
2 shadedboxes.qRT-PCRdatawerederivedfromthreebiological
andsaltstress,andwater-deficit-stressedleaf[10,31]andwater-
replicates.
deficit-stressedwholeberrytissues[24].Differencesingenemodel
ESTfrequenciesbetweenstressedandunstressedlibrarypairs(i.e.,
stressedberriescomparedwithunstressedberries)wereplotted
alongalog scaleaswell.TheSpearmanrankcorrelation,r,was gene models, whereas the remaining 307 singletons and
2 s
0.2047,withlikelihoodP=0.005).Filledandgraycirclesindicate 115 VvGI contigs did not match GSVIV gene models.
agreementanddisagreementindirectionalconcordance, The cDNA library normalization method was successful
respectively.ThetotalnumberofgenespresentineachCartesian
in generating a highly complex library, with 3449 (54%)
quadrantareshowningray-shadedboxes.
unique transcripts being represented by EST singletons.
Annotation of the 6424 non-redundant root transcripts
In order to verify the gene expression ratios deter- revealed 4505 (70%) had known functions, 455 (7%)
mined by microarray analysis, qRT-PCR was performed matched a previously annotated gene model, but the
on the set of genes listed in Additional file 5. These function was unclear, and 1464 (22.8%) had unknown
genes were selected at random and represented genes functions, with no homology matches to any previously
expressed preferentially in either leaf or berry tissues. described gene (Figure 6A). The functional categories
Relative mRNA expression for 17 and 22 transcripts was were assigned for the 4505 transcripts with known func-
assayed in drought-stressed and well-watered berry tis- tions (Figure 6B). Overall, the VVM normalized library
sue and leaf tissue, respectively. A linear regression of contained a high diversity of transcripts with the func-
the log -ratios of those genes found strong correlation tional categories of primary metabolism, signal transduc-
2
between transcript abundance measured by microarray tion, and transport systems being well represented
and qRT-PCR methods (Pearson correlation, r = 0.85) (Figure 6B).
and a very high degree of directional concordance (34/ Next, the 16,452 VVM Cabernet Sauvignon root ESTs
39 genes or 87%) (Figure 5). plus an additional 1657 ESTs from two Cabernet Sau-
vignon root libraries (Library ID 14445, 16696; Table 1)
Identification of root-enriched genes were analyzed for either root-specific or root-enriched
The 16,452 ESTs sequenced from the normalized abiotic transcripts. These root cDNA libraries were compared
stressed Cabernet Sauvignon root cDNA library (VVM) with a total of 291,233 ESTs from 114 libraries compris-
were matched to their VvGI ver. 6 consensus sequence ing the NCBI UniGene dataset http://www.ncbi.nlm.nih.
contigs [59] and, when possible, to the 8.4X genomic gov/UniGene/lbrowse2.cgi?TAXID=29760[48] with the
GSVIV gene/protein identifiers and matched with the exception of five EST libraries derived from in vitro or
annotation files associated with VitisNet [55], resulting cell cultures (Library ID 10498, 15513), mixed organ (e.
in the identification of 6424 non-redundant transcripts. g., root and leaf together) cDNA libraries (Library ID
Of these, 6002 were mapped successfully to 8.4X GSVIV 20007, 20010), or an amplified fragment length
Tillettetal.BMCPlantBiology2011,11:86 Page10of23
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underrepresentation of highly abundant root transcripts.
Evaluation of the functional categories of the 135 root-
enriched genes showed that genes for primary and sec-
ondary metabolism as well as transport processes were
more numerous compared with the entire root EST col-
lection (Figure 6C).
Validation of root-enriched genes
In order to confirm root expression patterns estimated
by EST frequency, the expression of a set of putative
root-specific or root-enriched genes was selected for
validation by qRT-PCR. Gene-specific primers were
designed for ten of the 135 highly root-enriched tran-
scripts. Genes were selected not only for those with very
high root EST count, but also for those gene with lower
frequencies, but still considered statistically significant.
The gene-specific primers used are listed in Additional
File 6. Relative transcript abundance for each gene was
tested within root and shoot tissue of Cabernet Sau-
vignon (Figure 7). Two-way ANOVA by gene and tissue
was performed, and both were significant (P < 0.0001).
Figure6FunctionalcategoriesofgenesintheVVMrootcDNA After ANOVA, individual Bonferroni corrected t-statis-
libraryandwithinaroot-enrichedsubset.Functional tics were computed for each individual gene between
assignmentsforgenesfromtheCabernetSauvignonrootEST root and shoot tissues. Of these ten transcripts, six were
library,VVM,weremadeusingVitisNetannotation.A)Proportionof
found to be significantly more abundant in roots than
genesidentifiedinVVMforwhichfunctionsareunclear,unknown, shoots by Student’s t-statistic (p < 0.01). Transcript
orareknownasdescribedwithinVitisNetannotation;B)
Classificationofthefunctionsofall4505genesfromtheabove abundances ranged from 3.8- to 730-times greater abun-
“known”categoryinVVM;C)thefunctionalassignmentsof135 dance in roots than shoots.
transcriptsestimatedtobedifferentiallyexpressedinroottissues The most highly root-enriched transcript encoded an
fromtheAudic-Claverietest[60]. uncharacterized Vitis tonoplast intrinsic protein TIP1;4
(GSVIVP00024394001) and was detected at 730-times
greater transcript abundance in roots than in shoot tis-
polymorphism (AFLP) cDNA library (Library ID 20099). sue. This correlates well with the estimated expression
Relative EST frequency counts were calculated as pre- by EST frequencies, where it was found with a fre-
viously described using weighted averages for the com- quency of ~33.6 tags per ten thousand (tp10k) in roots
bination of libraries grouped into either “root” or “non- compared with 0.1 tp10k in non-root tissues (57 root
root” groups. EST frequency counts for genes with two ESTs compared with 2 non-root ESTs). A resveratrol O-
or more ESTs within one or both of the library sets (sin- methyltransferase (ROMT, GSVIVP00018661001) that
gletons were removed) and corresponding differential was found with a frequency of 17.7 tp10k in roots (30
gene expression patterns were calculated with the root ESTs compared with 0 non-root EST) was
IDEG6 web tool using the Audic-Claverie statistic (AC), expressed 120-fold greater in root than in shoot as esti-
p-value < 0.01. Bonferroni multiple-testing correction mated by qRT-PCR. Similarly, a terpene synthase (TPS)
was applied to consider only p-values < 3.0 × 10-6 gene, (E, E)-alpha-farnesene synthase [61], was found
[53,60]. The comparison of root ESTs against all non- with a frequency of 13.6 tp10k in roots (23 root ESTs
root ESTs resulted in the initial identification of 255 compared with 0 non-root EST) and was 44-fold more
genes that had p-values below the significance threshold. abundant in root than shoot as assessed by qRT-PCR. A
Furthermore, the AC statistic identified 135 “root- cinnamyl-alcohol dehydrogenase (9 root ESTs compared
enriched” transcripts that showed greater frequencies in with 1 non-root EST) was expressed 27-fold greater in
root compared with non-root tissues as listed in Table roots than in shoots. A flavonol 3-O-glucosyltransferase
3. In addition, 119 of the 255 genes were identified as (10 root ESTs compared with 1 non-root EST) showed
being enriched in the non-root libraries. Because a nor- a 8.3-fold greater abundance in roots than in shoots.
malized root cDNA library was analyzed, these 119 Lastly, a Myb transcription factor-like a gene (5 root
genes were not considered further as the normalization ESTs compared with 0 non-root EST) was tested to
process was expected to result in a systematic evaluate the selected significance cutoff. This transcript